Overview

Dataset statistics

Number of variables15
Number of observations373
Missing cells21
Missing cells (%)0.4%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory43.8 KiB
Average record size in memory120.4 B

Variable types

Text2
Categorical6
Numeric7

Alerts

Hand has constant value ""Constant
MR Delay is highly overall correlated with VisitHigh correlation
EDUC is highly overall correlated with SESHigh correlation
eTIV is highly overall correlated with ASF and 1 other fieldsHigh correlation
ASF is highly overall correlated with eTIV and 1 other fieldsHigh correlation
Group is highly overall correlated with CDRHigh correlation
Visit is highly overall correlated with MR DelayHigh correlation
M/F is highly overall correlated with eTIV and 1 other fieldsHigh correlation
SES is highly overall correlated with EDUCHigh correlation
CDR is highly overall correlated with GroupHigh correlation
SES has 19 (5.1%) missing valuesMissing
MRI ID has unique valuesUnique
MR Delay has 150 (40.2%) zerosZeros

Reproduction

Analysis started2023-10-10 10:16:18.629162
Analysis finished2023-10-10 10:16:26.794261
Duration8.17 seconds
Software versionydata-profiling vv4.5.1
Download configurationconfig.json

Variables

Subject ID
Text

Distinct150
Distinct (%)40.2%
Missing0
Missing (%)0.0%
Memory size3.0 KiB
2023-10-10T15:46:27.114455image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Length

Max length9
Median length9
Mean length9
Min length9

Characters and Unicode

Total characters3357
Distinct characters14
Distinct categories3 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowOAS2_0001
2nd rowOAS2_0001
3rd rowOAS2_0002
4th rowOAS2_0002
5th rowOAS2_0002
ValueCountFrequency (%)
oas2_0070 5
 
1.3%
oas2_0127 5
 
1.3%
oas2_0073 5
 
1.3%
oas2_0048 5
 
1.3%
oas2_0147 4
 
1.1%
oas2_0067 4
 
1.1%
oas2_0036 4
 
1.1%
oas2_0037 4
 
1.1%
oas2_0017 4
 
1.1%
oas2_0183 4
 
1.1%
Other values (140) 329
88.2%
2023-10-10T15:46:27.706949image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
0 658
19.6%
2 450
13.4%
O 373
11.1%
A 373
11.1%
S 373
11.1%
_ 373
11.1%
1 244
 
7.3%
7 95
 
2.8%
4 86
 
2.6%
6 74
 
2.2%
Other values (4) 258
 
7.7%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 1865
55.6%
Uppercase Letter 1119
33.3%
Connector Punctuation 373
 
11.1%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 658
35.3%
2 450
24.1%
1 244
 
13.1%
7 95
 
5.1%
4 86
 
4.6%
6 74
 
4.0%
8 69
 
3.7%
3 67
 
3.6%
5 66
 
3.5%
9 56
 
3.0%
Uppercase Letter
ValueCountFrequency (%)
O 373
33.3%
A 373
33.3%
S 373
33.3%
Connector Punctuation
ValueCountFrequency (%)
_ 373
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 2238
66.7%
Latin 1119
33.3%

Most frequent character per script

Common
ValueCountFrequency (%)
0 658
29.4%
2 450
20.1%
_ 373
16.7%
1 244
 
10.9%
7 95
 
4.2%
4 86
 
3.8%
6 74
 
3.3%
8 69
 
3.1%
3 67
 
3.0%
5 66
 
2.9%
Latin
ValueCountFrequency (%)
O 373
33.3%
A 373
33.3%
S 373
33.3%

Most occurring blocks

ValueCountFrequency (%)
ASCII 3357
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 658
19.6%
2 450
13.4%
O 373
11.1%
A 373
11.1%
S 373
11.1%
_ 373
11.1%
1 244
 
7.3%
7 95
 
2.8%
4 86
 
2.6%
6 74
 
2.2%
Other values (4) 258
 
7.7%

MRI ID
Text

UNIQUE 

Distinct373
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Memory size3.0 KiB
2023-10-10T15:46:28.138313image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Length

Max length13
Median length13
Mean length13
Min length13

Characters and Unicode

Total characters4849
Distinct characters16
Distinct categories3 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique373 ?
Unique (%)100.0%

Sample

1st rowOAS2_0001_MR1
2nd rowOAS2_0001_MR2
3rd rowOAS2_0002_MR1
4th rowOAS2_0002_MR2
5th rowOAS2_0002_MR3
ValueCountFrequency (%)
oas2_0001_mr1 1
 
0.3%
oas2_0026_mr1 1
 
0.3%
oas2_0002_mr1 1
 
0.3%
oas2_0002_mr2 1
 
0.3%
oas2_0002_mr3 1
 
0.3%
oas2_0004_mr1 1
 
0.3%
oas2_0004_mr2 1
 
0.3%
oas2_0005_mr1 1
 
0.3%
oas2_0005_mr2 1
 
0.3%
oas2_0005_mr3 1
 
0.3%
Other values (363) 363
97.3%
2023-10-10T15:46:28.744344image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
_ 746
15.4%
0 658
13.6%
2 594
12.2%
1 394
8.1%
O 373
7.7%
A 373
7.7%
S 373
7.7%
M 373
7.7%
R 373
7.7%
3 125
 
2.6%
Other values (6) 467
9.6%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 2238
46.2%
Uppercase Letter 1865
38.5%
Connector Punctuation 746
 
15.4%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 658
29.4%
2 594
26.5%
1 394
17.6%
3 125
 
5.6%
4 101
 
4.5%
7 95
 
4.2%
6 74
 
3.3%
5 72
 
3.2%
8 69
 
3.1%
9 56
 
2.5%
Uppercase Letter
ValueCountFrequency (%)
O 373
20.0%
A 373
20.0%
S 373
20.0%
M 373
20.0%
R 373
20.0%
Connector Punctuation
ValueCountFrequency (%)
_ 746
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 2984
61.5%
Latin 1865
38.5%

Most frequent character per script

Common
ValueCountFrequency (%)
_ 746
25.0%
0 658
22.1%
2 594
19.9%
1 394
13.2%
3 125
 
4.2%
4 101
 
3.4%
7 95
 
3.2%
6 74
 
2.5%
5 72
 
2.4%
8 69
 
2.3%
Latin
ValueCountFrequency (%)
O 373
20.0%
A 373
20.0%
S 373
20.0%
M 373
20.0%
R 373
20.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII 4849
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
_ 746
15.4%
0 658
13.6%
2 594
12.2%
1 394
8.1%
O 373
7.7%
A 373
7.7%
S 373
7.7%
M 373
7.7%
R 373
7.7%
3 125
 
2.6%
Other values (6) 467
9.6%

Group
Categorical

HIGH CORRELATION 

Distinct3
Distinct (%)0.8%
Missing0
Missing (%)0.0%
Memory size3.0 KiB
Nondemented
190 
Demented
146 
Converted
37 

Length

Max length11
Median length11
Mean length9.6273458
Min length8

Characters and Unicode

Total characters3591
Distinct characters11
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowNondemented
2nd rowNondemented
3rd rowDemented
4th rowDemented
5th rowDemented

Common Values

ValueCountFrequency (%)
Nondemented 190
50.9%
Demented 146
39.1%
Converted 37
 
9.9%

Length

2023-10-10T15:46:28.974157image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-10-10T15:46:29.250264image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
ValueCountFrequency (%)
nondemented 190
50.9%
demented 146
39.1%
converted 37
 
9.9%

Most occurring characters

ValueCountFrequency (%)
e 1082
30.1%
n 563
15.7%
d 563
15.7%
t 373
 
10.4%
m 336
 
9.4%
o 227
 
6.3%
N 190
 
5.3%
D 146
 
4.1%
C 37
 
1.0%
v 37
 
1.0%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter 3218
89.6%
Uppercase Letter 373
 
10.4%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
e 1082
33.6%
n 563
17.5%
d 563
17.5%
t 373
 
11.6%
m 336
 
10.4%
o 227
 
7.1%
v 37
 
1.1%
r 37
 
1.1%
Uppercase Letter
ValueCountFrequency (%)
N 190
50.9%
D 146
39.1%
C 37
 
9.9%

Most occurring scripts

ValueCountFrequency (%)
Latin 3591
100.0%

Most frequent character per script

Latin
ValueCountFrequency (%)
e 1082
30.1%
n 563
15.7%
d 563
15.7%
t 373
 
10.4%
m 336
 
9.4%
o 227
 
6.3%
N 190
 
5.3%
D 146
 
4.1%
C 37
 
1.0%
v 37
 
1.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII 3591
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
e 1082
30.1%
n 563
15.7%
d 563
15.7%
t 373
 
10.4%
m 336
 
9.4%
o 227
 
6.3%
N 190
 
5.3%
D 146
 
4.1%
C 37
 
1.0%
v 37
 
1.0%

Visit
Categorical

HIGH CORRELATION 

Distinct5
Distinct (%)1.3%
Missing0
Missing (%)0.0%
Memory size3.0 KiB
1
150 
2
144 
3
58 
4
 
15
5
 
6

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters373
Distinct characters5
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row1
2nd row2
3rd row1
4th row2
5th row3

Common Values

ValueCountFrequency (%)
1 150
40.2%
2 144
38.6%
3 58
 
15.5%
4 15
 
4.0%
5 6
 
1.6%

Length

2023-10-10T15:46:29.456785image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-10-10T15:46:29.665792image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
ValueCountFrequency (%)
1 150
40.2%
2 144
38.6%
3 58
 
15.5%
4 15
 
4.0%
5 6
 
1.6%

Most occurring characters

ValueCountFrequency (%)
1 150
40.2%
2 144
38.6%
3 58
 
15.5%
4 15
 
4.0%
5 6
 
1.6%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 373
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
1 150
40.2%
2 144
38.6%
3 58
 
15.5%
4 15
 
4.0%
5 6
 
1.6%

Most occurring scripts

ValueCountFrequency (%)
Common 373
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
1 150
40.2%
2 144
38.6%
3 58
 
15.5%
4 15
 
4.0%
5 6
 
1.6%

Most occurring blocks

ValueCountFrequency (%)
ASCII 373
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
1 150
40.2%
2 144
38.6%
3 58
 
15.5%
4 15
 
4.0%
5 6
 
1.6%

MR Delay
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct201
Distinct (%)53.9%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean595.10456
Minimum0
Maximum2639
Zeros150
Zeros (%)40.2%
Negative0
Negative (%)0.0%
Memory size3.0 KiB
2023-10-10T15:46:29.863690image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median552
Q3873
95-th percentile1828
Maximum2639
Range2639
Interquartile range (IQR)873

Descriptive statistics

Standard deviation635.48512
Coefficient of variation (CV)1.0678546
Kurtosis0.2089126
Mean595.10456
Median Absolute Deviation (MAD)552
Skewness0.94503717
Sum221974
Variance403841.34
MonotonicityNot monotonic
2023-10-10T15:46:30.108469image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 150
40.2%
580 3
 
0.8%
1631 2
 
0.5%
647 2
 
0.5%
842 2
 
0.5%
846 2
 
0.5%
665 2
 
0.5%
1204 2
 
0.5%
567 2
 
0.5%
486 2
 
0.5%
Other values (191) 204
54.7%
ValueCountFrequency (%)
0 150
40.2%
182 1
 
0.3%
212 1
 
0.3%
248 1
 
0.3%
352 1
 
0.3%
365 1
 
0.3%
395 1
 
0.3%
403 1
 
0.3%
405 1
 
0.3%
432 1
 
0.3%
ValueCountFrequency (%)
2639 1
0.3%
2517 1
0.3%
2508 1
0.3%
2400 1
0.3%
2386 1
0.3%
2369 1
0.3%
2297 1
0.3%
2288 1
0.3%
2163 1
0.3%
2153 1
0.3%

M/F
Categorical

HIGH CORRELATION 

Distinct2
Distinct (%)0.5%
Missing0
Missing (%)0.0%
Memory size3.0 KiB
F
213 
M
160 

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters373
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowM
2nd rowM
3rd rowM
4th rowM
5th rowM

Common Values

ValueCountFrequency (%)
F 213
57.1%
M 160
42.9%

Length

2023-10-10T15:46:30.346676image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-10-10T15:46:30.495722image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
ValueCountFrequency (%)
f 213
57.1%
m 160
42.9%

Most occurring characters

ValueCountFrequency (%)
F 213
57.1%
M 160
42.9%

Most occurring categories

ValueCountFrequency (%)
Uppercase Letter 373
100.0%

Most frequent character per category

Uppercase Letter
ValueCountFrequency (%)
F 213
57.1%
M 160
42.9%

Most occurring scripts

ValueCountFrequency (%)
Latin 373
100.0%

Most frequent character per script

Latin
ValueCountFrequency (%)
F 213
57.1%
M 160
42.9%

Most occurring blocks

ValueCountFrequency (%)
ASCII 373
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
F 213
57.1%
M 160
42.9%

Hand
Categorical

CONSTANT 

Distinct1
Distinct (%)0.3%
Missing0
Missing (%)0.0%
Memory size3.0 KiB
R
373 

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters373
Distinct characters1
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowR
2nd rowR
3rd rowR
4th rowR
5th rowR

Common Values

ValueCountFrequency (%)
R 373
100.0%

Length

2023-10-10T15:46:30.639857image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-10-10T15:46:30.772672image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
ValueCountFrequency (%)
r 373
100.0%

Most occurring characters

ValueCountFrequency (%)
R 373
100.0%

Most occurring categories

ValueCountFrequency (%)
Uppercase Letter 373
100.0%

Most frequent character per category

Uppercase Letter
ValueCountFrequency (%)
R 373
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin 373
100.0%

Most frequent character per script

Latin
ValueCountFrequency (%)
R 373
100.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII 373
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
R 373
100.0%

Age
Real number (ℝ)

Distinct39
Distinct (%)10.5%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean77.013405
Minimum60
Maximum98
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size3.0 KiB
2023-10-10T15:46:30.896333image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Quantile statistics

Minimum60
5-th percentile65
Q171
median77
Q382
95-th percentile90
Maximum98
Range38
Interquartile range (IQR)11

Descriptive statistics

Standard deviation7.6409573
Coefficient of variation (CV)0.099215939
Kurtosis-0.41084744
Mean77.013405
Median Absolute Deviation (MAD)5
Skewness0.14170043
Sum28726
Variance58.384228
MonotonicityNot monotonic
2023-10-10T15:46:31.071835image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram with fixed size bins (bins=39)
ValueCountFrequency (%)
73 26
 
7.0%
75 22
 
5.9%
78 21
 
5.6%
80 20
 
5.4%
81 18
 
4.8%
71 18
 
4.8%
82 17
 
4.6%
76 16
 
4.3%
77 16
 
4.3%
68 14
 
3.8%
Other values (29) 185
49.6%
ValueCountFrequency (%)
60 2
 
0.5%
61 4
 
1.1%
62 4
 
1.1%
63 3
 
0.8%
64 3
 
0.8%
65 6
1.6%
66 10
2.7%
67 6
1.6%
68 14
3.8%
69 13
3.5%
ValueCountFrequency (%)
98 1
 
0.3%
97 1
 
0.3%
96 1
 
0.3%
95 1
 
0.3%
94 1
 
0.3%
93 3
0.8%
92 4
1.1%
91 4
1.1%
90 5
1.3%
89 7
1.9%

EDUC
Real number (ℝ)

HIGH CORRELATION 

Distinct12
Distinct (%)3.2%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean14.597855
Minimum6
Maximum23
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size3.0 KiB
2023-10-10T15:46:31.240010image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Quantile statistics

Minimum6
5-th percentile11
Q112
median15
Q316
95-th percentile18
Maximum23
Range17
Interquartile range (IQR)4

Descriptive statistics

Standard deviation2.8763395
Coefficient of variation (CV)0.1970385
Kurtosis-0.016035003
Mean14.597855
Median Absolute Deviation (MAD)3
Skewness-0.0259768
Sum5445
Variance8.2733287
MonotonicityNot monotonic
2023-10-10T15:46:31.366146image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram with fixed size bins (bins=12)
ValueCountFrequency (%)
12 103
27.6%
16 81
21.7%
18 64
17.2%
14 33
 
8.8%
13 27
 
7.2%
15 17
 
4.6%
20 13
 
3.5%
11 11
 
2.9%
8 9
 
2.4%
17 9
 
2.4%
Other values (2) 6
 
1.6%
ValueCountFrequency (%)
6 3
 
0.8%
8 9
 
2.4%
11 11
 
2.9%
12 103
27.6%
13 27
 
7.2%
14 33
 
8.8%
15 17
 
4.6%
16 81
21.7%
17 9
 
2.4%
18 64
17.2%
ValueCountFrequency (%)
23 3
 
0.8%
20 13
 
3.5%
18 64
17.2%
17 9
 
2.4%
16 81
21.7%
15 17
 
4.6%
14 33
 
8.8%
13 27
 
7.2%
12 103
27.6%
11 11
 
2.9%

SES
Categorical

HIGH CORRELATION  MISSING 

Distinct5
Distinct (%)1.4%
Missing19
Missing (%)5.1%
Memory size3.0 KiB
2.0
103 
1.0
88 
3.0
82 
4.0
74 
5.0
 
7

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters1062
Distinct characters7
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row2.0
2nd row2.0
3rd row3.0
4th row3.0
5th row4.0

Common Values

ValueCountFrequency (%)
2.0 103
27.6%
1.0 88
23.6%
3.0 82
22.0%
4.0 74
19.8%
5.0 7
 
1.9%
(Missing) 19
 
5.1%

Length

2023-10-10T15:46:31.515188image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-10-10T15:46:31.664391image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
ValueCountFrequency (%)
2.0 103
29.1%
1.0 88
24.9%
3.0 82
23.2%
4.0 74
20.9%
5.0 7
 
2.0%

Most occurring characters

ValueCountFrequency (%)
. 354
33.3%
0 354
33.3%
2 103
 
9.7%
1 88
 
8.3%
3 82
 
7.7%
4 74
 
7.0%
5 7
 
0.7%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 708
66.7%
Other Punctuation 354
33.3%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 354
50.0%
2 103
 
14.5%
1 88
 
12.4%
3 82
 
11.6%
4 74
 
10.5%
5 7
 
1.0%
Other Punctuation
ValueCountFrequency (%)
. 354
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 1062
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
. 354
33.3%
0 354
33.3%
2 103
 
9.7%
1 88
 
8.3%
3 82
 
7.7%
4 74
 
7.0%
5 7
 
0.7%

Most occurring blocks

ValueCountFrequency (%)
ASCII 1062
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
. 354
33.3%
0 354
33.3%
2 103
 
9.7%
1 88
 
8.3%
3 82
 
7.7%
4 74
 
7.0%
5 7
 
0.7%

MMSE
Real number (ℝ)

Distinct18
Distinct (%)4.9%
Missing2
Missing (%)0.5%
Infinite0
Infinite (%)0.0%
Mean27.342318
Minimum4
Maximum30
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size3.0 KiB
2023-10-10T15:46:31.783452image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Quantile statistics

Minimum4
5-th percentile20
Q127
median29
Q330
95-th percentile30
Maximum30
Range26
Interquartile range (IQR)3

Descriptive statistics

Standard deviation3.6832439
Coefficient of variation (CV)0.13470854
Kurtosis7.5158494
Mean27.342318
Median Absolute Deviation (MAD)1
Skewness-2.3660861
Sum10144
Variance13.566285
MonotonicityNot monotonic
2023-10-10T15:46:31.910423image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram with fixed size bins (bins=18)
ValueCountFrequency (%)
30 114
30.6%
29 91
24.4%
28 45
 
12.1%
27 32
 
8.6%
26 20
 
5.4%
25 12
 
3.2%
21 11
 
2.9%
23 11
 
2.9%
22 7
 
1.9%
20 7
 
1.9%
Other values (8) 21
 
5.6%
ValueCountFrequency (%)
4 1
 
0.3%
7 1
 
0.3%
15 2
 
0.5%
16 3
 
0.8%
17 5
1.3%
18 2
 
0.5%
19 3
 
0.8%
20 7
1.9%
21 11
2.9%
22 7
1.9%
ValueCountFrequency (%)
30 114
30.6%
29 91
24.4%
28 45
 
12.1%
27 32
 
8.6%
26 20
 
5.4%
25 12
 
3.2%
24 4
 
1.1%
23 11
 
2.9%
22 7
 
1.9%
21 11
 
2.9%

CDR
Categorical

HIGH CORRELATION 

Distinct4
Distinct (%)1.1%
Missing0
Missing (%)0.0%
Memory size3.0 KiB
0.0
206 
0.5
123 
1.0
41 
2.0
 
3

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters1119
Distinct characters5
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0.0
2nd row0.0
3rd row0.5
4th row0.5
5th row0.5

Common Values

ValueCountFrequency (%)
0.0 206
55.2%
0.5 123
33.0%
1.0 41
 
11.0%
2.0 3
 
0.8%

Length

2023-10-10T15:46:32.090158image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-10-10T15:46:32.265909image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
ValueCountFrequency (%)
0.0 206
55.2%
0.5 123
33.0%
1.0 41
 
11.0%
2.0 3
 
0.8%

Most occurring characters

ValueCountFrequency (%)
0 579
51.7%
. 373
33.3%
5 123
 
11.0%
1 41
 
3.7%
2 3
 
0.3%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 746
66.7%
Other Punctuation 373
33.3%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 579
77.6%
5 123
 
16.5%
1 41
 
5.5%
2 3
 
0.4%
Other Punctuation
ValueCountFrequency (%)
. 373
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 1119
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 579
51.7%
. 373
33.3%
5 123
 
11.0%
1 41
 
3.7%
2 3
 
0.3%

Most occurring blocks

ValueCountFrequency (%)
ASCII 1119
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 579
51.7%
. 373
33.3%
5 123
 
11.0%
1 41
 
3.7%
2 3
 
0.3%

eTIV
Real number (ℝ)

HIGH CORRELATION 

Distinct286
Distinct (%)76.7%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean1488.1287
Minimum1106
Maximum2004
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size3.0 KiB
2023-10-10T15:46:32.456102image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Quantile statistics

Minimum1106
5-th percentile1233.6
Q11357
median1470
Q31597
95-th percentile1817.4
Maximum2004
Range898
Interquartile range (IQR)240

Descriptive statistics

Standard deviation176.13929
Coefficient of variation (CV)0.11836294
Kurtosis-0.12534644
Mean1488.1287
Median Absolute Deviation (MAD)117
Skewness0.49688139
Sum555072
Variance31025.048
MonotonicityNot monotonic
2023-10-10T15:46:32.639174image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
1483 4
 
1.1%
1475 4
 
1.1%
1333 3
 
0.8%
1484 3
 
0.8%
1548 3
 
0.8%
1520 3
 
0.8%
1445 3
 
0.8%
1390 3
 
0.8%
1569 3
 
0.8%
1495 3
 
0.8%
Other values (276) 341
91.4%
ValueCountFrequency (%)
1106 1
0.3%
1123 1
0.3%
1143 1
0.3%
1151 1
0.3%
1154 1
0.3%
1159 1
0.3%
1165 1
0.3%
1169 1
0.3%
1171 1
0.3%
1174 1
0.3%
ValueCountFrequency (%)
2004 1
0.3%
1987 1
0.3%
1957 1
0.3%
1931 1
0.3%
1928 1
0.3%
1926 1
0.3%
1911 1
0.3%
1899 1
0.3%
1891 1
0.3%
1848 1
0.3%

nWBV
Real number (ℝ)

Distinct136
Distinct (%)36.5%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.72956836
Minimum0.644
Maximum0.837
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size3.0 KiB
2023-10-10T15:46:32.788491image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Quantile statistics

Minimum0.644
5-th percentile0.6746
Q10.7
median0.729
Q30.756
95-th percentile0.794
Maximum0.837
Range0.193
Interquartile range (IQR)0.056

Descriptive statistics

Standard deviation0.037135016
Coefficient of variation (CV)0.050899981
Kurtosis-0.41904446
Mean0.72956836
Median Absolute Deviation (MAD)0.028
Skewness0.23458692
Sum272.129
Variance0.0013790094
MonotonicityNot monotonic
2023-10-10T15:46:32.944247image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0.696 10
 
2.7%
0.739 9
 
2.4%
0.737 7
 
1.9%
0.695 7
 
1.9%
0.769 7
 
1.9%
0.748 7
 
1.9%
0.733 6
 
1.6%
0.757 6
 
1.6%
0.731 6
 
1.6%
0.75 6
 
1.6%
Other values (126) 302
81.0%
ValueCountFrequency (%)
0.644 1
0.3%
0.646 1
0.3%
0.652 1
0.3%
0.657 1
0.3%
0.66 2
0.5%
0.661 1
0.3%
0.662 1
0.3%
0.663 2
0.5%
0.665 1
0.3%
0.666 2
0.5%
ValueCountFrequency (%)
0.837 1
0.3%
0.827 1
0.3%
0.822 1
0.3%
0.819 1
0.3%
0.817 1
0.3%
0.812 1
0.3%
0.81 1
0.3%
0.806 2
0.5%
0.805 2
0.5%
0.801 2
0.5%

ASF
Real number (ℝ)

HIGH CORRELATION 

Distinct265
Distinct (%)71.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean1.1954611
Minimum0.876
Maximum1.587
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size3.0 KiB
2023-10-10T15:46:33.156477image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Quantile statistics

Minimum0.876
5-th percentile0.9656
Q11.099
median1.194
Q31.293
95-th percentile1.4222
Maximum1.587
Range0.711
Interquartile range (IQR)0.194

Descriptive statistics

Standard deviation0.13809196
Coefficient of variation (CV)0.11551355
Kurtosis-0.23211805
Mean1.1954611
Median Absolute Deviation (MAD)0.098
Skewness0.083450441
Sum445.907
Variance0.019069389
MonotonicityNot monotonic
2023-10-10T15:46:33.356075image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
1.184 5
 
1.3%
1.19 5
 
1.3%
1.134 4
 
1.1%
1.183 4
 
1.1%
1.174 3
 
0.8%
1.155 3
 
0.8%
1.291 3
 
0.8%
1.214 3
 
0.8%
1.202 3
 
0.8%
1.208 3
 
0.8%
Other values (255) 337
90.3%
ValueCountFrequency (%)
0.876 1
0.3%
0.883 1
0.3%
0.897 1
0.3%
0.909 1
0.3%
0.91 1
0.3%
0.911 1
0.3%
0.919 1
0.3%
0.924 1
0.3%
0.928 1
0.3%
0.95 1
0.3%
ValueCountFrequency (%)
1.587 1
0.3%
1.563 1
0.3%
1.535 1
0.3%
1.525 1
0.3%
1.521 1
0.3%
1.515 1
0.3%
1.506 1
0.3%
1.501 1
0.3%
1.499 1
0.3%
1.495 1
0.3%

Interactions

2023-10-10T15:46:24.824421image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-10-10T15:46:19.618615image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-10-10T15:46:20.584549image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-10-10T15:46:21.444484image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-10-10T15:46:22.325752image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-10-10T15:46:23.126194image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-10-10T15:46:23.976153image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-10-10T15:46:24.926195image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-10-10T15:46:19.730402image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-10-10T15:46:20.721256image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-10-10T15:46:21.571385image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-10-10T15:46:22.439457image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-10-10T15:46:23.266382image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-10-10T15:46:24.109085image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-10-10T15:46:25.035813image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-10-10T15:46:19.876381image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-10-10T15:46:20.839465image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-10-10T15:46:21.688856image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-10-10T15:46:22.545756image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-10-10T15:46:23.405737image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-10-10T15:46:24.243475image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-10-10T15:46:25.189437image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-10-10T15:46:20.044402image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-10-10T15:46:20.953412image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-10-10T15:46:21.853690image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-10-10T15:46:22.668524image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-10-10T15:46:23.526055image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-10-10T15:46:24.364393image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-10-10T15:46:25.375924image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-10-10T15:46:20.194250image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-10-10T15:46:21.076098image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-10-10T15:46:21.955557image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-10-10T15:46:22.775948image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-10-10T15:46:23.639304image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-10-10T15:46:24.475754image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-10-10T15:46:25.545713image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-10-10T15:46:20.326317image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-10-10T15:46:21.188944image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-10-10T15:46:22.072637image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-10-10T15:46:22.876053image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-10-10T15:46:23.728772image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-10-10T15:46:24.585880image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-10-10T15:46:25.715830image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-10-10T15:46:20.464427image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-10-10T15:46:21.322481image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-10-10T15:46:22.189186image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-10-10T15:46:22.994236image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-10-10T15:46:23.824503image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-10-10T15:46:24.698510image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Correlations

2023-10-10T15:46:33.546873image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
MR DelayAgeEDUCMMSEeTIVnWBVASFGroupVisitM/FSESCDR
MR Delay1.0000.1880.0570.0840.112-0.113-0.1120.1160.7560.0000.0000.057
Age0.1881.000-0.049-0.0360.051-0.483-0.0510.0730.0540.0000.0750.157
EDUC0.057-0.0491.0000.2290.230-0.011-0.2310.2150.0000.2690.5860.198
MMSE0.084-0.0360.2291.0000.0060.289-0.0060.4720.2270.1280.1260.469
eTIV0.1120.0510.2300.0061.000-0.208-1.0000.0720.0000.5680.2480.000
nWBV-0.113-0.483-0.0110.289-0.2081.0000.2080.1940.0550.3060.1720.181
ASF-0.112-0.051-0.231-0.006-1.0000.2081.0000.0990.0000.5720.2440.000
Group0.1160.0730.2150.4720.0720.1940.0991.0000.0370.2500.2180.669
Visit0.7560.0540.0000.2270.0000.0550.0000.0371.0000.0000.0000.000
M/F0.0000.0000.2690.1280.5680.3060.5720.2500.0001.0000.2390.256
SES0.0000.0750.5860.1260.2480.1720.2440.2180.0000.2391.0000.099
CDR0.0570.1570.1980.4690.0000.1810.0000.6690.0000.2560.0991.000

Missing values

2023-10-10T15:46:25.929557image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
A simple visualization of nullity by column.
2023-10-10T15:46:26.242754image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Nullity matrix is a data-dense display which lets you quickly visually pick out patterns in data completion.
2023-10-10T15:46:26.693576image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
The correlation heatmap measures nullity correlation: how strongly the presence or absence of one variable affects the presence of another.

Sample

Subject IDMRI IDGroupVisitMR DelayM/FHandAgeEDUCSESMMSECDReTIVnWBVASF
0OAS2_0001OAS2_0001_MR1Nondemented10MR87142.027.00.019870.6960.883
1OAS2_0001OAS2_0001_MR2Nondemented2457MR88142.030.00.020040.6810.876
2OAS2_0002OAS2_0002_MR1Demented10MR7512NaN23.00.516780.7361.046
3OAS2_0002OAS2_0002_MR2Demented2560MR7612NaN28.00.517380.7131.010
4OAS2_0002OAS2_0002_MR3Demented31895MR8012NaN22.00.516980.7011.034
5OAS2_0004OAS2_0004_MR1Nondemented10FR88183.028.00.012150.7101.444
6OAS2_0004OAS2_0004_MR2Nondemented2538FR90183.027.00.012000.7181.462
7OAS2_0005OAS2_0005_MR1Nondemented10MR80124.028.00.016890.7121.039
8OAS2_0005OAS2_0005_MR2Nondemented21010MR83124.029.00.517010.7111.032
9OAS2_0005OAS2_0005_MR3Nondemented31603MR85124.030.00.016990.7051.033
Subject IDMRI IDGroupVisitMR DelayM/FHandAgeEDUCSESMMSECDReTIVnWBVASF
363OAS2_0183OAS2_0183_MR3Nondemented3732FR68132.030.00.015060.7401.165
364OAS2_0183OAS2_0183_MR4Nondemented42107FR72132.030.00.015100.7231.162
365OAS2_0184OAS2_0184_MR1Demented10FR72163.024.00.513540.7331.296
366OAS2_0184OAS2_0184_MR2Demented2553FR73163.021.01.013510.7081.299
367OAS2_0185OAS2_0185_MR1Demented10MR80161.028.00.517040.7111.030
368OAS2_0185OAS2_0185_MR2Demented2842MR82161.028.00.516930.6941.037
369OAS2_0185OAS2_0185_MR3Demented32297MR86161.026.00.516880.6751.040
370OAS2_0186OAS2_0186_MR1Nondemented10FR61132.030.00.013190.8011.331
371OAS2_0186OAS2_0186_MR2Nondemented2763FR63132.030.00.013270.7961.323
372OAS2_0186OAS2_0186_MR3Nondemented31608FR65132.030.00.013330.8011.317